Global environmental impacts: criteria and indicator
“Best bet” Land-use Systems
Country reports
Alternatives To Slash-And-Burn In Indonesia
Unique id: IDAZAQWB
Source file: D:\Projects\ASB\ASB Country and Thematic reports\Indonesia PhaseII report\Part II-III.xml
Authors: Thomas P. Tomich, Meine van Noordwijk, Suseno Budidarsono, Andy Gillison, Trikurnianti Kusumanto, Daniel Murdiyarso, Fred Stolle, Ahmad M. Fagi, Iswandi Anas, A.F.S. Budiman, Kenneth Chomitz, Rebecca Elmhirst, Chip Fay, Hubert de Foresta, Dennis Garrity, Danan P. Hadi, Suryo Hardiwinoto, Kurniatun Hairiah, Genevieve Michon, Nu Nu San, Cheryl Palm, Soetjipto Partoharjono, Djuber Pasaribu, Eric Penot, Robert Simanungkalit, Martua Sirait, S.M. Sitompul, F.X. Susilo, David Thomas
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Land use at the forest
margins has an impact on two global environmental concerns: the net emissions
of greenhouse gasses (carbon dioxide, methane and nitrous oxide) which are
believed to have an impact on global climate change, and the conservation of
biodiversity.
The criterion for effects of land use change on net
greenhouse gas emissions can be explained by reference to the effects on
natural forests. When considered over large enough scales (in space and/or
time) the net carbon exchange between vegetation and atmosphere shows a small
flux, equal to the export of organic compounds in soil and water into
non-terrestrial ecosystems. The current C stocks in forest systems are large
relative to these fluxes and the main issue is in the fate of this stock during
land cover change. The two other greenhouse gasses of main global interest
(methane and nitrous oxide) can show net emissions or absorption, depending on
local soil conditions. Wetland sites (swamp forests as well as rice paddies)
generally emit methane, while upland forest soils can absorb and oxidize
methane. Nitrous oxide is emitted from all soils where mineral nitrogen is
present under relatively wet and warm conditions (so including natural
forests), but there may be absorption into green vegetation under certain
circumstances. Effects of land use change on greenhouse gas emissions can be measured
and expressed in units that allow comparison with industrial emissions, and in
the end an economic comparison can be made between the costs of reducing
emissions in various sectors of society. Hence, it is important to quantify the
effect of land use and land use change on these gasses as fluxes (amount of gas
molecules per unit land surface area and unit time).
For biodiversity the criterion is the maintenance of
global diversity and the role a particular area plays in that respect, but
there is no currency equivalent to the one for greenhouse gases -- diversity
measures can be expressed per unit area and per unit time, but can not be
converted easily to other units of area or extrapolated in time. For example,
if two areas both contain 100 different species, the combined area can contain
anywhere between 100 and 200 species, depending on the species overlap. The
contribution of a particular site to global biodiversity conservation depends
largely on the number of unique flora and fauna elements it contains. Although
survey data can show what plants and animals are currently present in a given
sampling area, the really important question of how many of these species (or
other taxonomic units or genes which are taken as the basis of comparison) would
survive over a time frame of X years, can not be directly assessed (Rosenzweig,
1995). Dynamics of local extinction and recolonization depend on the landscape
mosaic in which land use systems occur, as well as on the means of dispersal of
the organisms concerned. As a very first step into such a dynamic analysis,
local species richness is often used as an indicator, largely for lack of
better measures. Local species richness can not be compared across ecosystems
or even between continents, however, and the best we can do is express local
species richness for various land use types relative
to that of natural forest. We have to realize, however, that this ratio’s
can not be added or subtracted, and that their value probably depends on the
scale at which measurements were made. For example, previous comparisons of
plant diversity in rubber agroforests showed a local species richness of at
least half that of a natural forest, for a 40 m line transect. This does not
mean, however, that 1 ha of rubber agroforests will contain (let alone
conserve) half the species of 1 ha of natural forest; comparisons at the level
of Jambi province are even more uncertain, as it may well be that the 50%
forest species in the jungle rubber are generalists, occurring throughout the province
and the species not present in the jungle rubber are local specialists, with a
different diversity/scale relationship. Despite all these caveats, we will
present data here comparing biodiversity indices based on higher plants, which
indicate the similarity between sample sites in forest and non-forest, based on
a new technique of 'plant functional attributes' (Gillison 1998).
We also collected data on belowground biodiversity, as
this is an aspect on which little data exist. Parts of the belowground
biodiversity may be directly relevant to the farmer, as they effect 'ecological
service functions' (mineralization, soil structure maintenance, symbionts,
soil-borne diseases and their control).
II.1 Carbon stocks
Lowland tropical rain forests have the highest
standing biomass and aboveground carbon stocks of any vegetation in the world,
and total C stocks of rain forests are only equaled by the deepest peat
soils. Measurements in Jambi (Fig. II.1)
indicate that the total carbon stock of natural forests on the peneplain (above
a soil depth of 30 cm) can be up to 50 kg m-2or 500 Mg ha-1,
with roughly 80% in live trees, 10% in dead wood and 10% in the soil. In logged
forests (about 10 years after the logging event), live tree biomass is
substantially reduced, but there is more C in dead wood and at least as much in
the soil. In cassava fields total C stock can be reduced to about 10% of that
in the forest, but soil stocks are still similar to those in the forest. (These data have not been corrected for differences
in soil texture, however; compare the Corg/Cref ratio's described in chapter
III).
Conversion of rain forest to
other land uses, regardless of the technique used for conversion, is thus bound
to reduce the amount of C stored in terrestrial ecosystems. As the total net
release rate of carbon dioxide (CO2) into the atmosphere from land
use change and fossil fuel emissions exceeds the rate at which the ocean
surfaces can absorb additional CO2, atmospheric CO2
concentrations increase.
Figure II.1 Carbon stocks in a range of land uses in Jambi
In combinationwith other greenhouse gases, CO2
is held responsible for increasing the ‘greenhouse effect’ of reflecting
radiation from the earth, leading to changes in circulation patterns affecting
local climate, as well as causing an overall warming of the planet and an
ensuing rise in sea levels. Apart from accepting and adjusting to these climate
changes, the main mitigation options are to reduce fossil fuel use and slow
down or reverse the trend of declining C stocks in terrestrial ecosystems. In
all terrestrial ecosystems C sequestration (fixation) and C dissipation
(release) are approximately in equilibrium, with the vast majority of carbon
dioxide (CO2) molecules captured by photosynthesis in leaves during
the day being respired at night or during decomposition of litter. Only during
phases of build-up of biomass (aboveground or in roots) does the C stock of an
ecosystem increase. But in all natural
ecosystems, phases of decline and rejuvenation follow phases of growth. And in
managed ecosystems, harvest procedures arrest accumulation and usually lead to
a period of rejuvenation. In evaluating the C stock of land use systems we have
to choose a time frame: following CO2 molecules at a day or seasonal
scale is not necessary, as long as annual increments over the typical life span
of a system can be predicted.
Averaging the C stock over the life span of a system
gives a simple measure of its role in the global C balance, as long as
different stages of the system may be expected to occur in roughly proportional
areas at any point in time. If we can assign a typical ‘time-averaged Carbon
stock (Mg ha-1)’ to each land use type, we can directly evaluate how
‘land use change’ will lead to net C release or net C sequestration, depending
on the sign of the difference of
‘Cstock(after) – Cstock(before)’.This means that an evaluation of the C
stock of a land use depends on the
context and the types of comparisons made: compared to natural forest all other
land use types lead to net C release to the atmosphere, compared to continuous
annual crops, all other land uses lead to C sequestration.
Of particular relevance here may be
the C stock of shifting cultivation systems. Fig. II.2 shows how the
‘time-averaged C stock’ depends on the length of fallow and the rate of C
sequestration per year during the fallow. For very low land use intensities the
time-averaged C stock of shifting cultivation may approach that of a natural
forest, as the maximum C stock may be the same and the short episode of
slash-and-burn and production of food crops may resemble what happens after a
mature tree dies, falls and creates a gap. During intensification of shifting
cultivation systems, the time-averaged C stock will decrease rapidly (note the
logarithmic scale used for the Y axis in the graph). This analysis emphasizes
the systems context of forest clearing: if it is done in the context of
long-fallow rotations it will decrease the C stock much less than when it is
done for (supposedly) permanent food-crop cultivation.
Figure II.2 Time-averaged Carbon stock of shifting cultivation and fallow rotation
systems, as a function of the land use intensity R = Tc/ (Tc + Tf) where Tc is
length of cropping period (yr), Tf = length of fallow regrowth period (yr) and
Ic = annual C accumulation rate during fallow regrowth (Mg ha-1 yr-1)
To estimate the time-averaged C stock of the range
of land use systems evaluated as ‘alternatives to slash and burn’, we need the
following information:
-
Is it a rotational system where periodically whole
fields are cleared of vegetation to start a new cycle, or is it managed under
permanent vegetation cover?
-
What is the length of a single rotation cycle?
-
What is the rate of C sequestration per year during
the various stages of the cycle (e.g. during periods where annual food crops
are grown and during periods of fallow regrowth)?
-
Does the C stock reach a maximum at which annual C
sequestration levels off?
The land use systems chosen for evaluation all are
rotational in nature, except for the community managed forest with extraction
of non-timber forest products. Commercial logging (officially) consists of
logging episodes and periods where the forest can recover. All other land use
systems involve field clearing at the start of a new cycle, mostly using
slash-and-burn techniques of land clearing. Some of the rubber agroforests may
evolve into a stage of gap-level rejuvenation instead of field level clearing,
but the form chosen for evaluation of profitability (chapter IV) is a
rotational form. (We will come back to the issue of rotational versus permanent
agroforests in chapter IV).
The main remaining uncertainty is the annual rate of
C sequestration. The measurements of standing C stock in a range of land uses
at different ages since land clearing by slash-and-burn can be used to estimate
an average rate of C sequestration (Fig. II.3). In the figure three groups of
land use are distinguished:
-
logged-over forests; we have to make a rather
arbitrary decision on the effective age of the natural forest and the line
connecting the points of logged forest with natural forest may
overestimate C sequestration if logging
has done near-permanent damage to part
of the system (such a logging ramps and trails, see chapter III),
-
natural fallows (secondary forests), agroforests and
more intensive tree-crop production systems, which apparently accumulate at a
rate of about 2.5 Mg C
ha-1 yr-1
-
cassava/Imperata
systems where there is a negligible rate of C accumulation with age, presumably
because annual fires prevent the build up of C stocks in vegetation.
On the basis of these results time-averaged C stocks
were assigned to the land use types chosen for evaluation (Table II.1).
Fig. II.3 Carbon stock in aboveground biomass, surface litter and top 30 cm of
the soil, as a function of time since forest clearing (slash-and-burn) or
logging (left: whole data set, right: excluding the natural forest plots)
Table II.1 Time-averaged carbon stocks for land uses of the lowland peneplain;
three regression lines were used for the calculations (1 for forest, 2 for
agroforest and tree-crop plantations, 3 for cassava-imperata)
|
Land use system |
Line |
Maximum age (yr) |
Time
averaged C stock Mg
ha-1 |
|
Natural
forest |
1 |
120 |
254 |
|
Community-based forest management |
1 |
60 |
176 |
|
Commercial
logging |
1 |
40 |
150 |
|
Rubber
agroforests |
2 |
40 |
116 |
|
Rubber
agroforests with selected planting material |
2 |
30 |
103 |
|
Rubber
monoculture |
2 |
25 |
97 |
|
Oil palm
monoculture |
2 |
20 |
91 |
|
Upland
rice/ bush fallow rotation |
2 |
7 |
74 |
|
Cassava/Imperata rotation |
3 |
3 |
39 |
The values given here
contain many assumptions. As part of the ASB-Phase 2 activities in
When the same model was used, however, for data of
the KILLSOM/ADDSOM experiment at the BMSF station in the Lampung benchmark
area, agreement between measured and modeled was less convincing (Fig.? II.5);
the experimental data contain a substantial scatter, indicating
micro-variability not accounted for in the model. Simulations for Peltophorum inputs deviated more from
measured points, possibly due to the effect of polyphenolic substances not yet
accounted for in the Century model. Overall this experiment shows that none of
the organic input treatments is able to maintain the soil organic matter level
as it was at the start of the experiment, despite total inputs from litter of
at least 8 Mg ha-1. The main
reason for this effect may be a lack of soil macrofauna incorporating litter
into the soil -- nearly all inputs decompose at the soil surface and probably
contribute little to soil C pools. The century model can be modified to include
such effects of soil fauna, and this appears to be a priority area if a better
prediction of land use effects on soil carbon pools is needed. Better
predictions of soil carbon fractions, however, appear to be more relevant for
'sustainability' issues than they are for the total C balance. Changes in total
carbon stocks are clearly dominated by changes in aboveground biomass and a
better prediction of vegetation development is the key to improved modeling of
land use effects.
II.2 Greenhouse gas emissions
Measurements
of the net flux of methane and nitrous oxide were made in a wide range of land
use systems. Scaling up from point measurements to typical fluxes over the life
span of a land use system (similar to the time-averaged C stock) is not yet
possible, however. Day/night as well as seasonal rhythms have to be considered
to derive annual flux data, which should be combined for the year of forest
clearance and slash-and-burn, early re-growth etc.
Table II.2 summarizes the flux data obtained in the
wet and dry season for the land uses of our current evaluation. Methane
oxidation rates were higher in the dry than in the wet season. The low level of
NH4 and NO3 in Imperata
and cassava might have caused the low N2O emission from those
land-use systems. Data on N-mineralization,
therefore, have to be analyzed to explain the difference with nitrification or
denitrification pathways. For the current analysis we explored the relationship
between net methane flux and soil bulk density, and between nitrous oxide
emission and soil mineral N concentration, both modified by water-filled pore
space at the time of observation. Both relationships were weak, and may not
form sufficient basis for extrapolation between measuring points. A further
process-level analysis of causal factors is probably needed before GHG
emissions can be linked to models such as the Century model.
Figure II.4 The dynamics
of simulated (lines, S) and observed (points, O) for light (L), intermediate
(I), heavy (H) and total macro-organic matter (LIH = L + I + H) fractions when
lowland rainforest is converted to sugarcane (A), and the relationship between
observed and simulated L, I, H & LIH fractions (B) within 0-20 cm depth
under sugarcane. LIH = L + I + H
Figure II.5
Modeled and measured fate of soil
macro-organic matter fractions as part of a KILLSOM/ADDSOM experiment in
Lampung (Hairiah et al., 1996), where
Gliricidia litterfall is the main source of inputs; the overall decline is
still a consequence of pastconversion from forests and a lack of incorporation
of organic inputs into the soil
Data for methane oxidation and nitrous oxide
emission can be compared on the basis of their 'net radiative forcing’ (NRF) CO2 equivalent values (26 and 206, respectively).
It is obvious that removing above-ground carbon stock from forested land or
tree-based system will have a greater effect on global warming than that caused
by soil emissions. For the natural forest and rubber monoculture plots studied
the overall effect on net radiative forcing is negative (this means less global
warming, as more methane is oxidized than nitrous oxide emitted in NRF
equivalents). For the other land uses nitrous oxide emissions will have a
bigger impact on the greenhouse properties of the atmosphere than the methane
oxidation.
The last two columns in Table II.2
make a tentative comparison between the greenhouse gas fluxes of land uses per
se, with the effects of land use conversions based on change in time-averaged
carbon stock. When the difference in C stock is allocated to a 25 year time
period, and the data are converted to units of mol C m-2
yr-1, it becomes clear that changes in C stock will be
one to two orders of magnitude larger than the emissions in the land uses on a
stable basis. Obviously, the net climate effect for any land use when derived
from lowland rainforest is strongly negative (for the first 25 years), while
all land uses would have a substantial mitigating effect on climate change if
they replace the Imperata/cassava
cycle.
II.3 Belowground biodiversity
Data on belowground
biodiversity indicators are summarized in Table II.3. For most parameters the
differences between data collected in Jambi and those in Lampung were larger
than those between different land uses within each of these benchmark areas.
This is reflected in the probability values for the two 'main effects'
(province and land use) in table II.3; for a number of parameters land use
effects in Lampung differed from those in Jambi, reflected in a statistically
significant interaction.
Table II.2 Summary of net greenhouse gas emission effects from current land use
(methane and nitrous oxide) and land use change (carbon, allocated to a
25 year period)
|
Land
use system |
Time
averaged C stock, Mg
ha-1 |
Mean seasonal net
methane absorption, mg m-2 h-1 |
Mean
seasonal net
N2O emission, mg m-2 h-1 |
Net radiative forcing (C equivalents) mol
m-2 yr-1 |
|||||||||
|
|
|
Wet |
Dry |
Wet |
Dry |
soil emissions |
LU
conversion (25
years) |
||||||
|
from forest |
from
Imperata |
||||||||||||
|
Natural forest |
254 |
0.036 |
0.046 |
12.9 |
1.80 |
-0.03 |
0 |
n.a. |
|||||
|
Community-based forest management |
176 |
* |
* |
* |
* |
* |
26 |
n.a. |
|||||
|
Commercial logging |
150 |
0.044 |
0.050 |
17.8 |
3.60 |
0.06 |
35 |
n.a. |
|||||
|
Rubber agroforests |
116 |
0.035 |
* |
34.6 |
2.97 |
0.71 |
46 |
-26 |
|||||
|
Rubber agroforests with clonal material |
103 |
* |
0.029 |
* |
3.06 |
0.61 |
50 |
-22 |
|||||
|
Rubber monoculture |
97 |
0.009 |
0.060 |
6.1 |
0.43 |
-0.06 |
52 |
-20 |
|||||
|
Oil palm monoculture |
91 |
* |
* |
* |
* |
* |
54 |
-18 |
|||||
|
Upland rice/ bush fallow rotation |
74 |
* |
* |
* |
* |
* |
60 |
-12 |
|||||
|
Cassava/Imperata
rotation |
39 |
0.001 |
0.018 |
9.4 |
* |
0.24 |
72 |
0 |
|||||
n.a.= not applicable
*= no data
At first sight the effects
of land use on belowground biodiversity appear to be much smaller than
expected. Estimates of total population size for most microbial or soil
macrofauna groups are remarkably similar, although there are indications of
shifts between groups. For example, the Imperata
grasslands have the highest densities of earthworms and mycorrhizal spores,
while the forests have more ants and spiders in litter and soil samples (but
not in the pitfall traps). The total number of soil macrofauna groups present
in litter+soil samples was reduced in the Cassava+ Imperata samples, but for pitfall samples no difference was found
and for mycorrhizal spore diversity the highest values were found for this land
use type.
Table II.3 Results of
the surveys of indicators belowground biodiversity in five land uses of the
lowland peneplain of Sumatra; the statistical model tested for differences
between the two provinces (Lampung versus Jambi, confounded with a different
sampling date (September versus November)), five land use categories (Forest,
Agroforest, Rehabilitation (young tree-based systems), Cassava and Imperata,
respectively) and their interaction. For data on soil fauna the model included
a term for depth effects (surface litter and three soil layers), which is not
reported here
|
|
Prob
of F > value found |
Means
for land use types |
|||||||||
|
Province |
Land
use |
P
* L |
P |
all |
F |
A |
R |
C |
I |
||
|
Total
bacterial count (CFU g-1 of soil,
log) |
.0001 |
.057 |
.0003 |
L |
3.34 |
3.48 |
3.41 |
4.03 |
2.49 |
3.32 |
|
|
J |
4.03 |
4.00 |
3.84 |
3.81 |
4.21 |
4.50 |
|||||
|
J+L |
|
3.80 |
3.65 |
3.94 |
3.18 |
3.71 |
|||||
|
Fungi
(CFU g-1 of soil, log) |
.0001 |
.0008 |
.0001 |
L |
3.21 |
3.46 |
3.39 |
3.41 |
2.26 |
3.44 |
|
|
J |
4.28 |
3.31 |
4.10 |
5.05 |
5.40 |
5.11 |
|||||
|
J+L |
|
3.37 |
3.78 |
4.07 |
3.52 |
4.00 |
|||||
|
Respiration
(mg CO2-C kg-1 day-1, log) |
.0001 |
.0001 |
.38 |
L |
1.90 |
2.04 |
1.95 |
2.13 |
1.48 |
1.89 |
|
|
J |
2.65 |
2.83 |
2.70 |
2.56 |
2.33 |
2.54 |
|||||
|
J+L |
|
2.53 |
2.36 |
2.30 |
1.82 |
2.10 |
|||||
|
P-solubilizers
(CFU, g-1 of soil, log) |
.0001 |
.0323 |
.038 |
L |
-1.49 |
-1.10 |
-1.80 |
-0.47 |
-1.46 |
-2.38 |
|
|
J |
.376 |
-.063 |
0.779 |
0.897 |
-.446 |
0.464 |
|||||
|
J+L |
|
-.528 |
-.510 |
0.076 |
-1.21 |
||||||